For years, marketers have been talking about building a bridge between their existing customers, and the potential or yet-to-be-known customer.

Until recently, the two have rarely been connected. Agencies have separate marketing technology, data and analytics groups. Marketers themselves are often separated organizationally between “CRM” and “media” teams – sometimes even by a separate P&L.

Of course, there is a clearer dividing line between marketing tech and ad tech: personally identifiable information, or PII. Marketers today have two different types of data, from different places, with different rules dictating how it can be used.

In some ways, it has been natural for these two marketing disciplines to be separated, and some vendors have made a solid business from the work necessary to bridge PII data with web identifiers so people can be “onboarded” into cookies.

After all, marketers are interested in people, from the very top of the funnel when they visit a website as an anonymous visitor, all the way down the bottom of the funnel, after they are registered as a customer and we want to make them a brand advocate.

It would be great — magic even — if we could accurately understand our customers all the way through their various journeys (the fabled “360-degree view” of the customer) and give them the right message, at the right place and time. The combination of a strong CRM system and an enterprise data management platform (DMP) brings these two worlds together.

Much of this work is happening today, but it’s challenging with lots of ID matching, onboarding, and trying to connect systems that don’t ordinarily talk to one another. However, when CRM and DMP truly come together, it works.

What are some use cases?

Targeting people who haven’t opened an email

You might be one of those people who don’t open or engage with every promotional email in your inbox, or uses a smart filter to capture all of the marketing messages you receive every month.

To an email marketer, these people represent a big chunk of their database. Email is without a doubt the one of the most effective digital marketing channels, even though as few as 5% of people who engage are active buyers. It’s also relatively fairly straightforward way to predict return on advertising spend, based on historical open and conversion rates.

The connection between CRM and DMP enables the marketer to reach the 95% of their database everywhere else on the web, by connecting that (anonymized) email ID to the larger digital ecosystem: places like Facebook, Google, Twitter, advertising exchanges, and even premium publishers.

Understanding where the non-engaged email users are spending their time on the web, what they like, their behavior, income and buying habits is all now possible. The marketer has the “known” view of this customer from their CRM, but can also utilise vast sets of data to enrich their profile, and better engage them across the web.

Combining commerce and service data for journeys and sequencing

When we think of the customer journey, it gets complicated quickly. A typical ad campaign may feature thousands of websites, multiple creatives, different channels, a variety of different ad sizes and placements, delivery at different times of day and more.

When you map these variables against a few dozen audience segments, the combinatorial values get into numbers with a lot of zeros on the end. In other words, the typical campaign may have hundreds of millions of activities — and tens of millions of different ways a customer goes from an initial brand exposure all the way through to a purchase and the becoming a brand advocate.

How can you automatically discover the top 10 performing journeys?

Understanding which channels go together, and which sequences work best, can add up to tremendous lift for marketers.

For example, a media and entertainment company promoting a new show recently discovered that doing display advertising all week and then targeting the same people with a mobile “watch it tonight” message on the night of it aired produced a 20% lift in tune-in compared to display alone. Channel mix and sequencing work.

And that’s just the tip of the iceberg — we are only talking about web data.

What if you could look at a customer journey and find out that the call-to-action message resonated 20% higher one week after a purchase?

A pizza chain that tracks orders in its CRM system can start to understand the cadence of delivery (e.g. Thursday night is “pizza night” for the Johnson family) and map its display efforts to the right delivery frequency, ensuring the Johnsons receive targeted ads during the week, and a mobile coupon offer on Thursday afternoon, when it’s time to order.

How about a customer that has called and complained about a missed delivery, or a bad product experience? It’s probably a terrible idea to try and deliver a new product message when they have an outstanding customer ticket open. Those people can be suppressed from active campaigns, freeing up funds for attracting net new customers.

There are a lot of obvious use cases that come to mind when CRM data and web behavioral data is aligned at the people level. It’s simple stuff, but it works.

As marketers, we find ourselves seeking more and more precise targeting but, half the time, knowing when not to send a message is the more effective action.

As we start to see more seamless connections between CRM (existing customers) and DMPs (potential new customers), we imagine a world in which artificial intelligence can manage the cadence and sequence of messages based on all of the data — not just a subset of cookies, or email open rate.

As the organizational and technological barriers between CRM and DMP break down, we are seeing the next phase of what Gartner says is the “marketing hub” of interconnected systems or “stacks” where all of the different signals from current and potential customers come together to provide that 360-degree customer view.

It’s a great time to be a data-driven marketer!

Chris O’Hara is the head of global marketing for Krux, the Salesforce data management platform.

Where do People Fit into a World that Promises Endless Media Automation?

Ever since man tied a rope to an ox, there has been a relentless drive to automate work processes. Like primitive farming, digital media buying is a thankless, low-value task where results (and profits) do not often match the effort involved. Many companies are seeking to alleviate much of the process-heavy, detail-oriented tasks involved in finding, placing, serving, optimizing, tracking, and (most importantly) billing digital media campaigns with various degrees of success.

Let’s take the bleeding edge world of real-time audience buying. Trading desk managers are often working in multiple environments, on multiple screens. On a typical day, he may be logging into his AppNexus account, bidding on AdBrite for inventory, bidding for BlueKai stamps in that UI, looking for segmentation data in AdAdvisor, buying guaranteed audience on Legolas, trafficking ads in Atlas, and probably looking at some deep analytics data as well. If he is smart, he is probably managing that through a master platform, where he can look at performance of guaranteed display and even other media types. How efficient does that sound?

To me, it sounds like six logins too many. Putting aside the obvious fact that an abundance of technology doesn’t lead to efficiency (how’s “multitasking” working out for your 12 year old, by the way?), I wonder we aren’t asking too much of digital as a whole. How many ads have you clicked on lately? If the answer is zero, then you are in a large club. Broken down to its most basic level, we are working in a business that believes a 0.1% “success” rate is reason to celebrate. But the “click is a dead metric” some say. Really? Isn’t the whole point of a banner ad to drive someone to your website? When did that change?

All of this is simply to illustrate the larger point that the display advertising industry, for all of its supposed efficiencies, is really still in its very nascent stages. Navigating the commoditized world of banner advertising is still very much a human task, and the many machines we have created to wrestle the immense Internet into delivering an advertiser the perfect user are still primitive. For a short while longer, digital media is still the game of the agency media buyer…but not for long.

Let’s look at the areas in which smart media people add value to digital campaigns: site discovery, pricing, analytics and optimization, and billing.

Site Discovery

In the past, half the battle was knowing where to go. Which travel sites sold the most airline tickets? Which sites indexed most highly against men of a certain age, looking for their next automobile? What publisher did you call to get to IT professionals who made purchasing decisions on corporate laptops? Agencies had (and still have) plenty of institutional knowledge to help their clients partner with the right media to reach audiences efficiently and—even with the abundance of measurement tools out there—a lot of human guidance was needed. Now, given the ability to purchase that audience exactly using widely available data segments, the trick is simply knowing where to log in. I just found the latter IT professional segment in Bizo in less than 2 minutes. So the question becomes: how are you leveraging data and placement to achieve the desired result, and how efficiently are you doing it?

Pricing

It used to be that the big agencies could gain a huge pricing advantage through buying media in bulk. Holding company shops leveraged their power and muscled down publisher rate card by (sometimes) 80% or more with promised volume commitments, leaving smaller media agencies behind. Then, a funny thing happened: ad exchanges. All of the sudden, nearly all of the inventory in the world was available, and ready to be had in a second-price auction environment. Now, any Tom , Dick, and Harry with a network relationship could access relatively high quality impressions at prices that were guaranteed never to be too high (in a second-price auction, the winning bid is placed at the second highest price, meaning runaway “ceiling” bids are collapsed). Whoops. With their pricing advantage eliminated, large agencies did the next best thing: eliminated the middleman by building their own exchanges, which we have been calling “DSPs.” So, you don’t need human intervention to ensure pricing advantages.

Analytics and Optimization

What about figuring out what all the data means? After all, spreadsheets don’t optimize media campaigns. Don’t you need really smart, analytical media people to draw down click- and view-based data, sift through conversion metrics, and build attribution models? Maybe not. Not only are incredible algorithms taking that data and using machine learning to automatically optimize against clicks or conversions—but programmatic buying is slowly coming to all digital media as well. In the future, smart technology will enable planners to create dynamic media mixes that span guaranteed and real-time, and apply pricing across multiple methodologies (CPM, CPC, CPA). Much of that work is being done manually right now, but not for long.

Billing

Sadly, much of the digital media business comes down to billing at the end of the day. Media companies struggle tremendously with reconciling numbers across multiple systems, and agency ad servers don’t seem to speak the same language as publisher ones. The bulk of a media company’s time seems to be spend just trying to get paid, and an incredible amount of good salary gets burnt in the details of reconciliation and reporting. This is slowly changing, but the advent of good API development is starting to make the machines talk to each other more clearly. The platforms that can “plug in” ad serving and data APIs most easily have a lot to gain, and the industry as a whole will benefit from interoperability.

So, are people doomed in digital media? Not at all. There are going to be a lot less digital media buyers and planners needed—but what agencies are really going to need are smart media people. Right now, you need 4 people to manage 10 machines. In the near future, you will need 1 smart person to manage 1 platform—and the other three people can focus on something else. Maybe like talking to their clients.